Index(['PC1', 'PC2', 'PC3', 'PC4'], dtype='object')

Classification Report:
              precision    recall  f1-score   support

           0       0.54      0.20      0.29       215
           1       0.63      0.89      0.74       327

    accuracy                           0.61       542
   macro avg       0.58      0.54      0.51       542
weighted avg       0.59      0.61      0.56       542

Model Components

1. Data Preparation

• Selects relevant features:
  • OHLC prices
  • Volume
  • Moving averages
  • Technical indicators
• Converts target variable to binary format (BUY/SELL)

2. Model Training

• Splits data into train/test sets
• Trains logistic regression model
• Implements cross-validation

3. Evaluation Metrics

• Classification report showing:
  • Precision
  • Recall
  • F1-score
  • Support
• Confusion matrix visualization
• ROC curve with AUC score
• Feature importance plot

4. Visualizations

• Interactive Plotly plots for all visualizations
• Clear titles and labels
• Color-coded results for better interpretation
• Includes:
  • Confusion matrix heatmap
  • ROC curve plot
  • Feature importance bar chart
  • Signal prediction overlay on price chart

Fitting SARIMAX model...

c:\Users\coolm\Notebooks\.venv\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning:

Maximum Likelihood optimization failed to converge. Check mle_retvals

Training Random Forest model...

Random Forest Classification Report:
              precision    recall  f1-score   support

           0       0.96      0.97      0.97       215
           1       0.98      0.98      0.98       327

    accuracy                           0.97       542
   macro avg       0.97      0.97      0.97       542
weighted avg       0.97      0.97      0.97       542

SARIMAX and Random Forest Trading Model

Model Components

1. SARIMAX Modeling

• Uses close price as endogenous variable
• Uses other features as exogenous variables
• Predicts future prices

2. Random Forest Classification

• Uses SARIMAX predictions as additional features
• Predicts buy/sell signals
• Includes feature importance analysis

3. Visualizations

• SARIMAX predictions vs actual prices
• Confusion matrix
• ROC curve
• Feature importance
• Final trading signals on price chart

4. Evaluation Metrics

• Classification report
• ROC-AUC score
• Confusion matrix

Usage Instructions

1. Data Preparation

   • Ensure DataFrame has all required columns:
     • OHLC data (open, high, low, close)
     • Volume
     • Technical indicators (10_DMA, 30_DMA)

2. Model Execution

   run_combined_model(df)
   

3. Output

   • Displays all relevant plots
   • Prints performance metrics
   • Returns model objects and predictions

Approach Benefits

1. Two-Stage Prediction

   • First predicts price movements using SARIMAX
   • Uses predictions to enhance signal generation

2. Comprehensive Analysis

   • Provides detailed visualization of results
   • Includes multiple performance metrics

3. Model Interpretability

   • Feature importance analysis
   • Visual representation of predictions
   • Clear performance metrics

4. Easy Evaluation

   • Multiple evaluation metrics
   • Visual confirmation of predictions
   • Trading signal visualization